Much of the most important work being done in the field of neural net models, and indeed machine learning in general, is using very complex neural nets with many layers and features. This approach is often called deep architecture or deep learning. Human and animal learning occurs at a rate and depth that no machine can match. Many of the elements of biological learning still remain a mystery. One of the key areas of research, and one of the most useful in practical applications, is that of object recognition. This is something quite fundamental to living systems, and higher animals have evolved an extraordinary ability to learn complex relationships between objects. Biological brains have many layers; each synaptic event exists in a long chain of synaptic processes. In order to recognize complex objects, such as people's faces or handwritten digits, a fundamental task that is needed is to create a hierarchy of representation from the raw input to higher and...
Designing Machine Learning Systems with Python
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Designing Machine Learning Systems with Python
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Overview of this book
Machine learning is one of the fastest growing trends in modern computing. It has applications in a wide range of fields, including economics, the natural sciences, web development, and business modeling. In order to harness the power of these systems, it is essential that the practitioner develops a solid understanding of the underlying design principles.
There are many reasons why machine learning models may not give accurate results. By looking at these systems from a design perspective, we gain a deeper understanding of the underlying algorithms and the optimisational methods that are available. This book will give you a solid foundation in the machine learning design process, and enable you to build customised machine learning models to solve unique problems. You may already know about, or have worked with, some of the
off-the-shelf machine learning models for solving common problems such as spam detection or movie classification, but to begin solving more complex problems, it is important to adapt these models to your own specific needs. This book will give you this understanding and more.
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Table of Contents (16 chapters)
Designing Machine Learning Systems with Python
Credits
About the Author
About the Reviewer
www.PacktPub.com
Preface
Free Chapter
Thinking in Machine Learning
Tools and Techniques
Turning Data into Information
Models – Learning from Information
Linear Models
Neural Networks
Features – How Algorithms See the World
Learning with Ensembles
Design Strategies and Case Studies
Index
Customer Reviews